Purpose

After we’ve ingested the tract-level census data and queried the Valentine’s Day data, we’ll do some preliminary analyses to explore plotting on Leaflet.

Read in Valentine’s Day Data

valday <- readRDS("ChicagoCommute/RDA/valday.Rda")
valday %<>% mutate_at(c("pickup_centroid_latitude", "pickup_centroid_longitude"), as.numeric)
names(valday)

##  [1] "trip_id"                              
##  [2] "trip_start_timestamp"                 
##  [3] "trip_end_timestamp"                   
##  [4] "trip_seconds"                         
##  [5] "trip_miles"                           
##  [6] "pickup_community_area"                
##  [7] "dropoff_community_area"               
##  [8] "fare"                                 
##  [9] "tip"                                  
## [10] "additional_charges"                   
## [11] "trip_total"                           
## [12] "shared_trip_authorized"               
## [13] "trips_pooled"                         
## [14] "pickup_centroid_latitude"             
## [15] "pickup_centroid_longitude"            
## [16] "pickup_centroid_location.type"        
## [17] "pickup_centroid_location.coordinates" 
## [18] "dropoff_centroid_latitude"            
## [19] "dropoff_centroid_longitude"           
## [20] "dropoff_centroid_location.type"       
## [21] "dropoff_centroid_location.coordinates"
## [22] "dropoff_census_tract"                 
## [23] "pickup_census_tract"

Read in Census Data

race <- readRDS("ChicagoCommute/RDA/raceByTract.Rda")
income <- readRDS("ChicagoCommute/RDA/incomeByTract.Rda")

Appendix: Leaflet Practice and Exploration

Shapefile

Get the shapefile for Cook County, which is where Chicago is located.

shapefile <- tigris::tracts(state = "17", county = "031")
shapefile_race <- geo_join(shapefile, race,"TRACTCE", "tract", how="left")
# poly <- fortify(shapefile)
# ggmap(get_map("Chicago, IL", zoom=9, color="bw"), extent="device") +
  # geom_polygon(aes(x = long, y = lat, group = group), 
               # data = poly, colour = 'blue',
               # alpha = .4, size = .3)

# install.packages("leaflet.providers")
# load additional tile providers
library(leaflet.providers)

#make the palette
palette <- colorNumeric(
  palette="YlGnBu",
  domain = shapefile_race$Black.or.African.American
)

# make base map
base <- leaflet(leafletOptions(preferCanvas = TRUE)) %>%
  addProviderTiles(providers$CartoDB.Positron,
                   options = providerTileOptions(
  updateWhenZooming = FALSE,      # map won't update tiles until zoom is done
  updateWhenIdle = TRUE)) %>%
  addPolygons(data=shapefile_race, color="grey", weight=1); base

# add in black population
with_black <- base %>%
  #note: should probably do this based on proportion in tract
              addPolygons(data=shapefile_race,
                fillColor=~palette(Black.or.African.American), 
                stroke=FALSE) %>%
              addLegend(
                pal = palette,
                values = shapefile_race$Black.or.African.American,
                position = "bottomright",
                title = "Population of 
                Black/African American Persons"
      ); with_black

Sunset in Chicago, IL on Valentine’s Day 2019 was at 5:22 PM. Let’s filter the data based on that. I’m still having issues with slow rendering, even with making the sample only 1000 points.

library(lubridate)
sunset <- lubridate::make_datetime(year=2019L, month=2L, day=14L,
                         hour=17L, min=22L, sec=0, tz=NULL)

daytime <- filter(valday, trip_start_timestamp <= sunset)
daytime <- daytime[sample(nrow(daytime), size=1000),]

with_rides <- with_black %>%
                addCircleMarkers(
                  lng= daytime$pickup_centroid_longitude,
                  lat = daytime$pickup_centroid_latitude, 
                  clusterOptions = markerClusterOptions(),
                  stroke = FALSE,
                  radius = 1.5
                  ); with_rides